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An AI-driven marketplace matching solution designed to intelligently connect buyers and sellers, service providers and seekers, or demand and supply in real time through ChatGPT-powered contextual intelligence.
Project Overview
A fast-growing digital marketplace platform faced challenges in efficiently matching users on both sides of the marketplace. Traditional rule-based matching logic failed to account for user intent, preferences, availability, and contextual factors—leading to poor match quality and lower conversion rates.
The organization partnered with us to build a ChatGPT-powered marketplace matching application capable of understanding user requirements through natural language, analyzing historical behavior, and dynamically generating high-quality matches in real time.
The objective was to create an intelligent, scalable matching engine that improves user satisfaction, accelerates transactions, and strengthens marketplace liquidity.
Business Challenge
Poor Match Relevance
Rule-based matching often produced low-quality matches, leading to abandoned conversations and drop-offs.
Limited Understanding of User Intent
The legacy system could not interpret nuanced requirements, preferences, or conversational inputs.
Cold-Start Problem
New users lacked sufficient data, making early matches ineffective and reducing onboarding success.
Low Conversion Rates
Inefficient matching directly impacted deal closures, bookings, and transaction volumes.
Scalability Constraints
The platform struggled to handle increasing user activity and complex matching logic at scale.
Solution
ChatGPT-Powered Intent Understanding
Integrated ChatGPT to capture and interpret user intent, preferences, constraints, and conversational inputs with high accuracy.
Intelligent Matching Algorithms
Developed AI-driven matching models combining semantic similarity, behavioral data, and contextual signals to generate optimal matches.
Cold-Start Optimization
Leveraged ChatGPT-based profiling and dynamic questioning to improve early-stage matching for new users.
Real-Time Match Scoring
Implemented real-time scoring and ranking to prioritize the most relevant matches dynamically.
Continuous Learning Loop
The system continuously refined matching accuracy using feedback, interaction outcomes, and transaction data.
Marketplace Analytics Dashboard
Provided insights into match quality, conversion rates, liquidity metrics, and user behavior trends.
Scalable Cloud Architecture
Deployed on a cloud-native infrastructure designed to support high concurrency and evolving marketplace complexity.
Technology Stack Used
- Python
- OpenAI / ChatGPT API
- TensorFlow
- PyTorch
- FastAPI
- React.js
- PostgreSQL
- Redis
- Apache Kafka
- Docker
- Kubernetes
- AWS Lambda
- Amazon S3
- GitLab CI/CD
Client Review
“The ChatGPT-powered matching system has dramatically improved the quality of connections on our platform. Users find relevant matches faster, engagement has increased, and overall marketplace activity has grown significantly. The AI-driven approach has transformed our core marketplace experience.”
Nitin Agarwal is a veteran in custom software development. He is fascinated by how software can turn ideas into real-world solutions. With extensive experience designing scalable and efficient systems, he focuses on creating software that delivers tangible results. Nitin enjoys exploring emerging technologies, taking on challenging projects, and mentoring teams to bring ideas to life. He believes that good software is not just about code; it’s about understanding problems and creating value for users. For him, great software combines thoughtful design, clever engineering, and a clear understanding of the problems it’s meant to solve.
based on 1200+ reviews
